Abstract
The performance of a simple, spatially-lumped, rainfall–streamflow model is compared with that of a more complex, spatially-distributed model. In terms of two model-fit statistics it is shown that for two catchments in Brazil (about 30,000 km 2 and 34,000 km 2) with different flow regimes, the simpler catchment models, which are unit hydrograph-based and require only rainfall, streamflow and air temperature data for calibration, perform about as well as more complex catchment models that require additional information from satellite images and digitized maps of elevation, land-use and soils. Simple catchment models are applied in forecasting mode, using daily rainfall forecasts from a regional weather forecasting model. The value of the rainfall forecasts, relative to the case where rainfall is known, is assessed for both catchments. The results are discussed in the context of on-going work to compare different modelling approaches for many other Brazilian catchments, and to apply improved forecasting algorithms based on the simple modelling approach to the same, and other, catchments.
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